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Adejumo P, Thangaraj P, Dhingra LS, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz HM, Khera R. A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters. medRxiv 2024:2024.03.30.24305095. [PMID: 38633789 PMCID: PMC11023654 DOI: 10.1101/2024.03.30.24305095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Introduction Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. We developed and validated a deep learning-based natural language processing (NLP) strategy to extract functional status assessments from unstructured clinical notes. Methods We identified 26,577 HF patients across outpatient services at Yale New Haven Hospital (YNHH), Greenwich Hospital (GH), and Northeast Medical Group (NMG) (mean age 76.1 years; 52.0% women). We used expert annotated notes from YNHH for model development/internal testing and from GH and NMG for external validation. The primary outcomes were NLP models to detect (a) explicit New York Heart Association (NYHA) classification, (b) HF symptoms during activity or rest, and (c) functional status assessment frequency. Results Among 3,000 expert-annotated notes, 13.6% mentioned NYHA class, and 26.5% described HF symptoms. The model to detect NYHA classes achieved a class-weighted AUROC of 0.99 (95% CI: 0.98-1.00) at YNHH, 0.98 (0.96-1.00) at NMG, and 0.98 (0.92-1.00) at GH. The activity-related HF symptom model achieved an AUROC of 0.94 (0.89-0.98) at YNHH, 0.94 (0.91-0.97) at NMG, and 0.95 (0.92-0.99) at GH. Deploying the NYHA model among 166,655 unannotated notes from YNHH identified 21,528 (12.9%) with NYHA mentions and 17,642 encounters (10.5%) classifiable into functional status groups based on activity-related symptoms. Conclusions We developed and validated an NLP approach to extract NYHA classification and activity-related HF symptoms from clinical notes, enhancing the ability to track optimal care and identify trial-eligible patients.
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Affiliation(s)
- Philip Adejumo
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Phyllis Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Xinyu Zhou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Cynthia Brandt
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Hua Xu
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
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Aminorroaya A, Dhingra LS, Camargos AP, Shankar SV, Khunte A, Sangha V, McNamara RL, Haynes N, Oikonomou EK, Khera R. Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD). medRxiv 2024:2024.03.18.24304477. [PMID: 38562867 PMCID: PMC10984075 DOI: 10.1101/2024.03.18.24304477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Introduction Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). Research Methods and Analysis This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. Ethics and Dissemination All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).
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Affiliation(s)
- Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Sumukh Vasisht Shankar
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Engineering Science, Oxford University, Oxford, OX1 3PJ, United Kingdom
| | - Robert L McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Norrisa Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven,Connecticut, USA
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Khera R, Aminorroaya A, Dhingra LS, Thangaraj PM, Camargos AP, Bu F, Ding X, Nishimura A, Anand TV, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Kaur G, Lau WC, Li J, Li K, Liu Y, Lu Y, Man KK, Matheny ME, Mathioudakis N, McLeggon JA, McLemore MF, Minty E, Morales DR, Nagy P, Ostropolets A, Pistillo A, Phan TP, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager SL, Simon KR, Viernes B, Yang J, Yin C, You SC, Zhou JJ, Ryan PB, Schuemie MJ, Krumholz HM, Hripcsak G, Suchard MA. Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302354. [PMID: 38370787 PMCID: PMC10871374 DOI: 10.1101/2024.02.05.24302354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding National Institutes of Health, United States Department of Veterans Affairs.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Fan Bu
- Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA
| | - Xiyu Ding
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Tara V Anand
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Yi Chai
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong
| | - Shounak Chattopadhyay
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael Cook
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guneet Kaur
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Wallis Cy Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Kenneth Kc Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Paul Nagy
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anna Ostropolets
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | | | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | - Lauren Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Joseph Ross
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Elise Ruan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Sarah L Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, UK
| | - Katherine R Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin Viernes
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jianxiao Yang
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
- Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, 8560, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
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Sangha V, Dhingra LS, Oikonomou E, Aminorroaya A, Sikand NV, Sen S, Krumholz HM, Khera R. Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning. medRxiv 2023:2023.12.23.23300490. [PMID: 38234746 PMCID: PMC10793540 DOI: 10.1101/2023.12.23.23300490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is the leading cause of sudden cardiac death in young adults. HCM can be identified using an electrocardiogram (ECG) raw voltage data and deep learning approaches, but their point-of-care application is limited by the inaccessibility of these signal data. We developed a deep learning-based approach that overcomes this limitation and detects HCM from images of 12-lead ECGs across layouts. Methods We identified ECGs from patients with HCM features present on cardiac magnetic resonance imaging (CMR) or those within 30 days of an echocardiogram documenting thickened interventricular septum (end-diastolic interventricular septum thickness > 15mm). Patients with CMR-confirmed HCM were considered as cases during the final model evaluation. The model was validated within clinical settings at YNHH and externally on ECG images from the prospective, population-based UK Biobank cohort. We localized class-discriminating signals in ECG images using gradient-weighted class activation mapping. Results Overall, 124,553 ECGs from 66,987 individuals (HCM cases and controls) were used for model development. The model demonstrated high discrimination for HCM across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROC] 0.96) and external sets of ECG images from UK Biobank (AUROC 0.94). A positive screen for HCM was associated with a 100-fold higher odds of CMR-confirmed HCM (OR 102.4, 95% Confidence Interval, 57.4 - 182.6) in the held-out set. Class-discriminative patterns localized to the anterior and lateral leads (V4-V5). Conclusions We developed and externally validated a deep learning model that identifies HCM from ECG images with excellent discrimination. This approach represents an automated, efficient, and accessible screening strategy for HCM.
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Affiliation(s)
- Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, Oxford University, Oxford, UK
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Evangelos Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Nikhil V Sikand
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Sounok Sen
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Lu Y, Liu Y, Dhingra LS, Caraballo C, Mahajan S, Massey D, Spatz ES, Sharma R, Rodriguez F, Watson KE, Masoudi FA, Krumholz HM. National Trends in Racial and Ethnic Disparities in Use of Recommended Therapies in Adults with Atherosclerotic Cardiovascular Disease, 1999-2020. JAMA Netw Open 2023; 6:e2345964. [PMID: 38039001 PMCID: PMC10692850 DOI: 10.1001/jamanetworkopen.2023.45964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023] Open
Abstract
Importance Despite efforts to improve the quality of care for patients with atherosclerotic cardiovascular disease (ASCVD), it is unclear whether the US has made progress in reducing racial and ethnic differences in utilization of guideline-recommended therapies for secondary prevention. Objective To evaluate 21-year trends in racial and ethnic differences in utilization of guideline-recommended pharmacological medications and lifestyle modifications among US adults with ASCVD. Design, Setting, and Participants This cross-sectional study includes data from the National Health and Nutrition Examination Survey between 1999 and 2020. Eligible participants were adults aged 18 years or older with a history of ASCVD. Data were analyzed between March 2022 and May 2023. Exposure Self-reported race and ethnicity. Main Outcome and Measures Rates and racial and ethnic differences in the use of guideline-recommended pharmacological medications and lifestyle modifications. Results The study included 5218 adults with a history of ASCVD (mean [SD] age, 65.5 [13.2] years, 2148 women [weighted average, 44.2%]), among whom 1170 (11.6%) were Black, 930 (7.7%) were Hispanic or Latino, and 3118 (80.7%) were White in the weighted sample. Between 1999 and 2020, there was a significant increase in total cholesterol control and statin use in all racial and ethnic subgroups, and in angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) utilization in non-Hispanic White individuals and Hispanic and Latino individuals (Hispanic and Latino individuals: 17.12 percentage points; 95% CI, 0.37-37.88 percentage points; P = .046; non-Hispanic White individuals: 12.14 percentage points; 95% CI, 6.08-18.20 percentage points; P < .001), as well as smoking cessation within the Hispanic and Latino population (-27.13 percentage points; 95% CI, -43.14 to -11.12 percentage points; P = .002). During the same period, the difference in smoking cessation between Hispanic and Latino individuals and White individuals was reduced (-24.85 percentage points; 95% CI, -38.19 to -11.51 percentage points; P < .001), but racial and ethnic differences for other metrics did not change significantly. Notably, substantial gaps persisted between current care and optimal care throughout the 2 decades of data analyzed. In the period of 2017 to 2020, optimal regimens were observed in 47.4% (95% CI, 39.3%-55.4%), 48.7% (95% CI, 36.7%-60.6%), and 53.0% (95% CI, 45.6%-60.4%) of Black, Hispanic and Latino, and White individuals, respectively. Conclusions and Relevance In this cross-sectional study of US adults with ASCVD, significant disparities persisted between current care and optimal care, surpassing any differences observed among demographic groups. These findings highlight the critical need for sustained efforts to bridge these gaps and achieve better outcomes for all patients, regardless of their racial and ethnic backgrounds.
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Affiliation(s)
- Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Yuntian Liu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Lovedeep Singh Dhingra
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - César Caraballo
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Shiwani Mahajan
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Daisy Massey
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Erica S Spatz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Richa Sharma
- Department of Neurology, Yale School of Public Health, New Haven, Connecticut
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, California
| | - Karol E Watson
- David Geffen School of Medicine, University of California, Los Angeles
| | | | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
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6
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Khera R, Dhingra LS, Aminorroaya A, Li K, Zhou JJ, Arshad F, Blacketer C, Bowring MG, Bu F, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Horban S, Lau WCY, Li J, Liu Y, Lu Y, Man KKC, Matheny ME, Mathioudakis N, McLemore MF, Minty E, Morales DR, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada JD, Pratt N, Reyes C, Ross JS, Seager S, Shah N, Simon K, Wan EYF, Yang J, Yin C, You SC, Schuemie MJ, Ryan PB, Hripcsak G, Krumholz H, Suchard MA. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Med 2023; 2:e000651. [PMID: 37829182 PMCID: PMC10565313 DOI: 10.1136/bmjmed-2023-000651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 07/07/2023] [Indexed: 10/14/2023]
Abstract
Objective To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Mary G Bowring
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fan Bu
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael Cook
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA
| | - Talita Duarte-Salles
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- The University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott Horban
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wallis CY Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kenneth KC Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Paul Nagy
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrea Pistillo
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Jose D Posada
- Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Carlen Reyes
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Sarah Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
- Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA
| | - Katherine Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric YF Wan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China
| | - Jianxiao Yang
- Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea)
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea)
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Harlan Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Dhingra LS, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. Am J Cardiol 2023; 203:136-148. [PMID: 37499593 PMCID: PMC10865722 DOI: 10.1016/j.amjcard.2023.06.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
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Affiliation(s)
| | - Miles Shen
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Internal Medicine
| | - Anjali Mangla
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
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8
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Singh RB, Singhal S, Sinha S, Cho J, Nguyen AXL, Dhingra LS, Kaur S, Sharma V, Agarwal A. Ocular complications of plasma cell dyscrasias. Eur J Ophthalmol 2023; 33:1786-1800. [PMID: 36760117 PMCID: PMC10472748 DOI: 10.1177/11206721231155974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023]
Abstract
Plasma cell dyscrasias are a wide range of severe monoclonal gammopathies caused by pre-malignant or malignant plasma cells that over-secrete an abnormal monoclonal antibody. These disorders are associated with various systemic findings, including ophthalmological disorders. A search of PubMed, EMBASE, Scopus and Cochrane databases was performed in March 2021 to examine evidence pertaining to ocular complications in patients diagnosed with plasma cell dyscrasias. This review outlines the ocular complications associated with smoldering multiple myeloma and monoclonal gammopathy of undetermined significance, plasmacytomas, multiple myeloma, Waldenström's macroglobulinemia, systemic amyloidosis, Polyneuropathy, Organomegaly, Endocrinopathy, Monoclonal gammopathy and Skin changes (POEMS) syndrome, and cryoglobulinemia. Although, the pathological mechanisms are not completely elucidated yet, wide-ranging ocular presentations have been identified over the years, evolving both the anterior and posterior segments of the eye. Moreover, the presenting symptoms also help in early diagnosis in asymptomatic patients. Therefore, it is imperative for the treating ophthalmologist and oncologist to maintain a high clinical suspicion for identifying the ophthalmological signs and diagnosing the underlying disease, preventing its progression through efficacious treatment strategies.
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Affiliation(s)
- Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Great Ormond Street Institute of Child Health, University College London, London, UK
- Discipline of Ophthalmology and Visual Sciences, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
| | - Sachi Singhal
- Department of Internal Medicine, Crozer-Chester Medical Center, Upland, PA, USA
| | - Shruti Sinha
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Junsang Cho
- Department of Ophthalmology, Vanderbilt Eye Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Lovedeep Singh Dhingra
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Snimarjot Kaur
- Department of Pediatrics, Yale-New Haven Hospital, New Haven, CT, USA
| | - Vasudha Sharma
- Department of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana, India
| | - Aniruddha Agarwal
- Department of Ophthalmology, University of Maastricht, Maastricht, the Netherlands
- Department of Ophthalmology, The Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
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9
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Liu Y, Herrin J, Huang C, Khera R, Dhingra LS, Dong W, Mortazavi BJ, Krumholz HM, Lu Y. Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys. J Am Med Inform Assoc 2023; 30:943-952. [PMID: 36905605 PMCID: PMC10114129 DOI: 10.1093/jamia/ocad035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/21/2023] [Accepted: 03/02/2023] [Indexed: 03/12/2023] Open
Abstract
OBJECTIVE Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys. MATERIALS AND METHODS We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES). Maximal oxygen uptake (VO2 max), measured through a submaximal exercise test, served as the gold standard measure for CRF in this study. We applied multiple ML algorithms to build 2 models: a parsimonious model using commonly available interview and examination data, and an extended model additionally incorporating variables from Dual-Energy X-ray Absorptiometry (DEXA) and standard laboratory tests in clinical practice. Key predictors were identified using Shapley additive explanation (SHAP). RESULTS Among the 5668 NHANES participants in the study population, 49.9% were women and the mean (SD) age was 32.5 years (10.0). The light gradient boosting machine (LightGBM) had the best performance across multiple types of supervised ML algorithms. Compared with the best existing nonexercise algorithms that could be applied to the NHANES, the parsimonious LightGBM model (RMSE: 8.51 ml/kg/min [95% CI: 7.73-9.33]) and the extended LightGBM model (RMSE: 8.26 ml/kg/min [95% CI: 7.44-9.09]) significantly reduced the error by 15% and 12% (P < .001 for both), respectively. DISCUSSION The integration of ML and national data source presents a novel approach for estimating cardiovascular fitness. This method provides valuable insights for cardiovascular disease risk classification and clinical decision-making, ultimately leading to improved health outcomes. CONCLUSION Our nonexercise models provide improved accuracy in estimating VO2 max within NHANES data as compared to existing nonexercise algorithms.
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Affiliation(s)
- Yuntian Liu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jeph Herrin
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Chenxi Huang
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Rohan Khera
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lovedeep Singh Dhingra
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Weilai Dong
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Bobak J Mortazavi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA
- Center for Remote Health Technologies and Systems, Texas A&M University, College Station, Texas, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
| | - Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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Awasthi R, Rakholia V, Agarwal S, Dhingra LS, Nagori A, Kaur H, Sethi T. Estimating the Impact of Health Systems Factors on Antimicrobial Resistance in Priority Pathogens. J Glob Antimicrob Resist 2022; 30:133-142. [DOI: 10.1016/j.jgar.2022.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 11/15/2022] Open
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Pandey R, Gautam V, Pal R, Bandhey H, Dhingra LS, Misra V, Sharma H, Jain C, Bhagat K, Arushi, Patel L, Agarwal M, Agrawal S, Jalan R, Wadhwa A, Garg A, Agrawal Y, Rana B, Kumaraguru P, Sethi T. A machine learning application for raising WASH awareness in the times of COVID-19 pandemic. Sci Rep 2022; 12:810. [PMID: 35039533 PMCID: PMC8764038 DOI: 10.1038/s41598-021-03869-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/19/2021] [Indexed: 12/27/2022] Open
Abstract
The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.
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Affiliation(s)
- Rohan Pandey
- Shiv Nadar University, Noida, Uttar Pradesh, India
| | | | - Ridam Pal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Harsh Bandhey
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Lovedeep Singh Dhingra
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India.,All India Institute of Medical Sciences, New Delhi, India
| | - Vihaan Misra
- Netaji Subhas University of Technology, Dwarka, New Delhi, India
| | - Himanshu Sharma
- GL Bajaj Institute of Tech and Management, Greater Noida, Uttar Pradesh, India
| | - Chirag Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Kanav Bhagat
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Arushi
- All India Institute of Medical Sciences, New Delhi, India
| | - Lajjaben Patel
- All India Institute of Medical Sciences, New Delhi, India
| | - Mudit Agarwal
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Rishabh Jalan
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Akshat Wadhwa
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Ayush Garg
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Yashwin Agrawal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Bhavika Rana
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Ponnurangam Kumaraguru
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India
| | - Tavpritesh Sethi
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, Okhla Industrial Estate, Phase III, New Delhi, 110020, India.
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12
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Lu Y, Liu Y, Dhingra LS, Massey D, Caraballo C, Mahajan S, Spatz ES, Onuma O, Herrin J, Krumholz HM. National Trends in Racial and Ethnic Disparities in Antihypertensive Medication Use and Blood Pressure Control Among Adults With Hypertension, 2011-2018. Hypertension 2021; 79:207-217. [PMID: 34775785 DOI: 10.1161/hypertensionaha.121.18381] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Poor hypertension awareness and underuse of guideline-recommended medications are critical factors contributing to poor hypertension control. Using data from 8095 hypertensive people aged ≥18 years from the National Health and Nutrition Examination Survey (2011-2018), we examined recent trends in racial and ethnic differences in awareness and antihypertensive medication use, and their association with racial and ethnic differences in hypertension control. Between 2011 and 2018, age-adjusted hypertension awareness declined for Black, Hispanic, and White individuals, but the 3 outcomes increased or did not change for Asian individuals. Compared with White individuals, Black individuals had a similar awareness (odds ratio, 1.20 [0.96-1.45]) and overall treatment rates (1.04 [0.84-1.25]), and received more intensive antihypertensive medication if treated (1.41 [1.27-1.56]), but had a lower control rate (0.72 [0.61-0.83]). Asian and Hispanic individuals had significantly lower awareness rates (0.69 [0.52-0.85] and 0.74 [0.59-0.89]), overall treatment rates (0.72 [0.57-0.88] and 0.69 [0.55-0.82]), received less intensive medication if treated (0.60 [0.50-0.72] and 0.86 [0.75-0.96]), and had lower control rates (0.66 [0.54-0.79] and 0.69 [0.57-0.81]). The racial and ethnic differences in awareness, treatment, and control persisted over the study period and were consistent across age, sex, and income strata. Lower awareness and treatment were significantly associated with lower control in Asian and Hispanic individuals (P<0.01 for all) but not in Black individuals. These findings highlight the need for interventions to improve awareness and treatment among Asian and Hispanic individuals, and more investigation into the downstream factors that may contribute to the poor hypertension control among Black individuals.
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Affiliation(s)
- Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Yuntian Liu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Lovedeep Singh Dhingra
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Daisy Massey
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - César Caraballo
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Shiwani Mahajan
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Erica S Spatz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.).,Department of Epidemiology (Chronic Disease), Yale School of Public Health, New Haven CT (E.S.S)
| | - Oyere Onuma
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Jeph Herrin
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.)
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (Y. Lu, Y. Liu, L.S.D., D.M., C.C., S.M., E.S.S., O.O., J.H., H.M.K.).,Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (H.M.K.)
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Agarwal M, Arushi A, Dhingra LS, Patel LJ, Agrawal S, Srivastava P, Tripathi M, Srivastava A, Bhatia R, Singh MB, Prasad K, Vibha D, Vishnu VY, Rajan R, Pandit AK, Singh RK, Gupta A, Radhakrishnan DM, Das A, Ramanujam B, Agarwal A, Elavarasi A. Patient Experience of a Neurology Tele-Follow-Up Program Initiated During the Coronavirus Disease 2019 Pandemic: A Questionnaire-Based Study. Telemed Rep 2021; 2:88-96. [PMID: 35720744 PMCID: PMC8989087 DOI: 10.1089/tmr.2020.0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 06/15/2023]
Abstract
Background: Teleneurology consultations can be highly advantageous since neurological diseases and disabilities often limit patient's access to health care, particularly in a setting where they need to travel long distances for specialty consults. Patient satisfaction is an important outcome assessing success of a telemedicine program. Materials and Methods: A cross-sectional study was conducted to determine satisfaction and perception of patients toward an audio call based teleneurology follow-up initiated during the coronavirus disease 2019 pandemic. Primary outcomes were satisfaction to tele-consult, and proportion of patients preferring telemedicine for future follow-up. Results: A total of 261 patients who received tele-consult were enrolled. Satisfaction was highest for domain technological quality, followed by patient-physician dialogue (PPD) and least to quality of care (QoC). Median (interquartile range) patient satisfaction on a 5-point Likert scale was 4 (3-5). Eighty-five (32.6%; 95% confidence interval 26.9-38.6%) patients preferred telemedicine for future follow-up. Higher overall satisfaction was associated with health condition being stable/better, change in treatment advised on tele-consult, diagnosis not requiring follow-up examination, higher scores on domains QoC and PPD (p < 0.05). Future preference for telemedicine was associated with patient him-/herself consulting with doctor, less duration of follow-up, higher overall satisfaction, and higher scores on domain QoC (p < 0.05). On thematic analysis, telemedicine was found convenient, reduced expenditure, and had better physician attention; in-person visits were comprehensive, had better patient-physician relationship, and better communication. Discussion: Patient satisfaction was lower in our study than what has been observed earlier, which may be explained by the primitive nature of our platform. Several variables related to the patients' disease process have an effect on patient satisfaction. Conclusion: Development of robust, structured platforms is necessary to fully utilize the potential of telemedicine in developing countries.
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Affiliation(s)
- Mudit Agarwal
- MBBS, All India Institute of Medical Sciences, New Delhi, India
| | - Arushi Arushi
- MBBS, All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Padma Srivastava
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Achal Srivastava
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Rohit Bhatia
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Mamta Bhushan Singh
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Kameshwar Prasad
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Deepti Vibha
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | - Roopa Rajan
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Awadh Kishor Pandit
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Rajesh Kumar Singh
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Anu Gupta
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | - Animesh Das
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Bhargavi Ramanujam
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Ayush Agarwal
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
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14
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Khera R, Dhingra LS, Jain S, Krumholz HM. An Evaluation of the Vulnerable Physician Workforce in the USA During the Coronavirus Disease-19 Pandemic. J Gen Intern Med 2020; 35:3114-3116. [PMID: 32495101 PMCID: PMC7269617 DOI: 10.1007/s11606-020-05854-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 04/13/2020] [Indexed: 11/10/2022]
Affiliation(s)
- Rohan Khera
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | | | - Snigdha Jain
- Department of Internal Medicine, Division of Pulmonary and Critical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, Section of Cardiovascular Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
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15
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Khera R, Dhingra LS, Jain S, Krumholz HM. An Evaluation of the Vulnerable Physician Workforce in the United States During the Coronavirus Disease-19 Pandemic. medRxiv 2020:2020.03.26.20044263. [PMID: 32511623 PMCID: PMC7276050 DOI: 10.1101/2020.03.26.20044263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
BackgroundThe coronavirus disease-19 (COVID-19) pandemic threatens to overwhelm the healthcare resources of the country, but also poses a personal hazard to healthcare workers, including physicians. To address the potential impact of excluding physicians with a high risk of adverse outcomes based on age, we evaluated the current patterns of age of licensed physicians across the United States.MethodsWe compiled information from the 2018 database of actively licensed physicians in the Federation of State Medical Boards (FSMB) across the US. Both at a national- and the state-level, we assessed the number and proportion of physicians who would be at an elevated risk due to age over 60 years.ResultsOf the 985,026 licensed physicians in the US, 235857 or 23.9% were aged 25-40 years, 447052 or 45.4% are 40-60 years, 191794 or 19.5% were 60-70 years, and 106121 or 10.8% were 70 years or older. Age was not reported in 4202 or 0.4% of physicians. Overall, 297915 or 30.2% of physicians were 60 years of age or older, 246167 (25.0%) 65 years and older, and 106121 (10.8%) 70 years or older. States in the US reported that a median 5470 licensed physicians (interquartile range [IQR], 2394 to 10108) were 60 years of age or older. Notably, states of North Dakota (n=1180) and Vermont (n = 1215) had the lowest and California (n=50786) and New York (n=31582) the highest number of physicians over the age of 60 years (Figure 1). Across states, the median proportion of physicians aged 60 years and older was 28.9% (IQR, 27.2%, 31.4%), and ranged between 25.9% for Nebraska to 32.6% for New Mexico (Figure 2).DiscussionOlder physicians represent a large proportion of the US physician workforce, particularly in states with the worst COVID-19 outbreak. Therefore, their exclusion from patient care will be impractical. Optimizing care practices by limiting direct patient contact of physicians vulnerable to adverse outcomes from COVID-19, potentially by expanding their participation in telehealth may be a strategy to protect them.
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Affiliation(s)
- Rohan Khera
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Snigdha Jain
- Department of Internal Medicine, Division of Pulmonary and Critical Care, University of Texas Southwestern Medical Center, Dallas, TX
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
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16
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Abstract
Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture body surface temperature with the potential to reveal underlying perfusion disturbance in shock. In this study, we automate early detection and prediction of shock using machine learning upon thermal images obtained in a pediatric intensive care unit of a tertiary care hospital. 539 images were recorded out of which 253 had concomitant measurement of continuous intra-arterial blood pressure, the gold standard for shock monitoring. Histogram of oriented gradient features were used for machine learning based region-of-interest segmentation that achieved 96% agreement with a human expert. The segmented center-to-periphery difference along with pulse rate was used in longitudinal prediction of shock at 0, 3, 6 and 12 hours using a generalized linear mixed-effects model. The model achieved a mean area under the receiver operating characteristic curve of 75% at 0 hours (classification), 77% at 3 hours (prediction) and 69% at 12 hours (prediction) respectively. Since hemodynamic shock associated with critical illness and infectious epidemics such as Dengue is often fatal, our model demonstrates an affordable, non-invasive, non-contact and tele-diagnostic decision support system for its reliable detection and prediction.
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Affiliation(s)
- Aditya Nagori
- CSIR-Institute of Genomics and Integrative Biology, New Delhi, 110007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Lovedeep Singh Dhingra
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, 110029, India
| | - Ambika Bhatnagar
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, 110029, India
| | - Rakesh Lodha
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, 110029, India
| | - Tavpritesh Sethi
- All India Institute of Medical Sciences, Department of Pediatrics, New Delhi, 110029, India.
- Indraprastha Institute of Information Technology Delhi, 110020, Delhi, India.
- Stanford University, School of Medicine, Stanford, 94305, CA, USA.
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